7,567 research outputs found
Maximum Resilience of Artificial Neural Networks
The deployment of Artificial Neural Networks (ANNs) in safety-critical
applications poses a number of new verification and certification challenges.
In particular, for ANN-enabled self-driving vehicles it is important to
establish properties about the resilience of ANNs to noisy or even maliciously
manipulated sensory input. We are addressing these challenges by defining
resilience properties of ANN-based classifiers as the maximal amount of input
or sensor perturbation which is still tolerated. This problem of computing
maximal perturbation bounds for ANNs is then reduced to solving mixed integer
optimization problems (MIP). A number of MIP encoding heuristics are developed
for drastically reducing MIP-solver runtimes, and using parallelization of
MIP-solvers results in an almost linear speed-up in the number (up to a certain
limit) of computing cores in our experiments. We demonstrate the effectiveness
and scalability of our approach by means of computing maximal resilience bounds
for a number of ANN benchmark sets ranging from typical image recognition
scenarios to the autonomous maneuvering of robots.Comment: Timestamp research work conducted in the project. version 2: fix some
typos, rephrase the definition, and add some more existing wor
full-FORCE: A Target-Based Method for Training Recurrent Networks
Trained recurrent networks are powerful tools for modeling dynamic neural
computations. We present a target-based method for modifying the full
connectivity matrix of a recurrent network to train it to perform tasks
involving temporally complex input/output transformations. The method
introduces a second network during training to provide suitable "target"
dynamics useful for performing the task. Because it exploits the full recurrent
connectivity, the method produces networks that perform tasks with fewer
neurons and greater noise robustness than traditional least-squares (FORCE)
approaches. In addition, we show how introducing additional input signals into
the target-generating network, which act as task hints, greatly extends the
range of tasks that can be learned and provides control over the complexity and
nature of the dynamics of the trained, task-performing network.Comment: 20 pages, 8 figure
EFFICIENCY AND PRODUCTIVITY GROWTH IN INDIAN BANKING
This paper attempts to examine technical efficiency and productivity performance of Indian scheduled commercial banks, for the period 1979-2008. We model a multiple output/multiple input technology production frontier using semiparametric estimation methods. The endogenity of multiple outputs is addressed by semi parametric estimates in part by introducing multivariate kernel estimators for the joint distribution of the multiple outputs and correlated random effects. Output is measured as the rupee value of total loans and total investments at the end of the year. The estimates provide robust inferences of the productivity and efficiency gains due to economic reforms.Banking, Frontier efficiency, Productivity
Sumoylation silences the plasma membrane leak K+ channel K2P1.
Reversible, covalent modification with small ubiquitin-related modifier proteins (SUMOs) is known to mediate nuclear import/export and activity of transcription factors. Here, the SUMO pathway is shown to operate at the plasma membrane to control ion channel function. SUMO-conjugating enzyme is seen to be resident in plasma membrane, to assemble with K2P1, and to modify K2P1 lysine 274. K2P1 had not previously shown function despite mRNA expression in heart, brain, and kidney and sequence features like other two-P loop K+ leak (K2P) pores that control activity of excitable cells. Removal of the peptide adduct by SUMO protease reveals K2P1 to be a K+-selective, pH-sensitive, openly rectifying channel regulated by reversible peptide linkage
Challenges in video based object detection in maritime scenario using computer vision
This paper discusses the technical challenges in maritime image processing
and machine vision problems for video streams generated by cameras. Even well
documented problems of horizon detection and registration of frames in a video
are very challenging in maritime scenarios. More advanced problems of
background subtraction and object detection in video streams are very
challenging. Challenges arising from the dynamic nature of the background,
unavailability of static cues, presence of small objects at distant
backgrounds, illumination effects, all contribute to the challenges as
discussed here
Sarcopenia in male patients with head and neck cancer receiving chemoradiotherapy: a longitudinal pilot study
A novel correlation model for horizontal axis wind turbines operating at high-interference flow regimes
Driven by economics-of-scale factors, wind-turbine rotor sizes have increased formidably in recent years. Larger rotors with lighter blades of increased flexibility will experiment substantially higher levels of deformation. Future turbines will also incorporate advanced control strategies to widen the range of wind velocities over which energy is captured. These factors will extend turbine operational regimes, including flow states with high interference factors. In this paper we derive a new empirical relation to both improve and extend the range of Blade Element Momentum (BEM) models, when applied to high interference-factor regimes. In most BEM models, these flow regimes are modeled using empirical relations derived from experimental data. However, an empirical relation that best represents these flow states is still missing. The new relation presented in this paper is based on data from numerical experiments performed on an actuator disk model, and implemented in the context of a novel model of the BEM family called the DRD-BEM (Dynamic Rotor Deformation—BEM), recently introduced in Ponta, et al., 2016. A detailed description of the numerical experiments is presented, followed by DRD-BEM simulation results for the case of the benchmark NREL-5MW Reference Wind Turbine with this new polynomial curve incorporated
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